Dynamics of prescriptivism and lexical borrowings in Contemporary French
Bibliographic record
Abstract
In France and Québec, language contact with English is often perceived as a source of negative influence on French. Terminological commissions working under the supervision of the Académie française and the Office québécois de la langue française are tasked to replace foreign words and expressions with French terminology that is mandatory in all government publications. However, the general public is merely encouraged to comply with these recommendations: the actual use of these top-down lexical innovations remains to be established. Using examples from newspaper and social media corpora, this study investigates how speakers comply with the use of prescribed French terminology, including emblematic lexical innovations such as courriel and mot-dièse, rather than their English equivalents.\n\nThe research combines quantitative and qualitative methodologies applied on large corpora of formal and informal written texts from France and Québec. The first quantitative component comprises newspaper articles from 2000 to 2017 in order to examine whether purist recommendations are implemented in formal written language. Time is treated with a new dynamic approach: the probability of use of a prescribed term is estimated three years before and three years after official prescription. 54 target terms are selected from the lexical fields of computer science, entertainment industry and telecommunication. The second quantitative component consists of tweets published from January 2010 to December 2016, targeting 4 lexical items recurring with high frequency in the newspaper corpus. Statistical analyses were implemented on variables of gender (male or female users), social media influence score, and urban population size, complemented with mapping the diffusion of lexical innovations in France and Québec. The third, qualitative component explores reactions to prescription in tweets and newspapers, examined with sentiment analysis and close reading.\n\nThe analyses reveal that prescription is primarily effective when it follows already attested usage, as demonstrated in the estimated probability of use in both the newspaper and the social media corpora. Conservative newspapers show higher proportions of recommended terminology, especially as compared to newspapers specializing in technology. Language users express more prescriptive attitudes in Québec than they do in France, which signals the perception of failed top-down intervention on French social media, but a successful one among Québécois users. These results corroborate previous scholarship on regional variation toward prescription, partly due to English being a prestigious foreign language in France as opposed to a native language in Québec where Francophone speakers showcase stronger linguistic purism against it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".